Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach

Juan Pinto-Ríos, Felipe Calderón, Ariel Leiva, Gabriel Hermosilla, Alejandra Beghelli, Danilo Bórquez-Paredes, Astrid Lozada, Nicolás Jara, Ricardo Olivares, Gabriel Saavedra

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

4 Citas (Scopus)

Resumen

A deep reinforcement learning (DRL) approach is applied, for the first time, to solve the routing, modulation, spectrum, and core allocation (RMSCA) problem in dynamic multicore fiber elastic optical networks (MCF-EONs). To do so, a new environment was designed and implemented to emulate the operation of MCF-EONs - taking into account the modulation format-dependent reach and intercore crosstalk (XT) - and four DRL agents were trained to solve the RMSCA problem. The blocking performance of the trained agents was compared through simulation to 3 baselines RMSCA heuristics. Results obtained for the NSFNet and COST239 network topologies under different traffic loads show that the best-performing agent achieves, on average, up to a four-times decrease in blocking probability with respect to the best-performing baseline heuristic method.

Idioma originalInglés
Número de artículo4140594
PublicaciónComplexity
Volumen2023
DOI
EstadoPublicada - 2023

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